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Journal articles on the topic 'Indic Script'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

PANDEY, Krishna Kumar, and Smita JHA. "Tracing the Identity and Ascertaining the Nature of Brahmi-derived Devanagari Script." Acta Linguistica Asiatica 9, no. 1 (2019): 59–73. http://dx.doi.org/10.4312/ala.9.1.59-73.

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Current research exploits the orthographic design of Brahmi-derived scripts (also called Indic scripts), particularly the Devanagari script. Earlier works on orthographic nature of Brahmi-derived scripts fail to create a consensus among epigraphists, historians or linguists, and thus have been identified by various names, like semi-syllabic, subsyllabic, semi-alphabetic, alphasyllabary or abugida. On the contrary, this paper argues that Brahmi-derived scripts should not be categorized as scripts with overlapping features of alphabetic and syllabic properties as these scripts are neither alphab

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S, K. Shreesha, and B. Anita H. "Classification of North and South Indian Handwritten Scripts using Gabor Wavelet Features." Indian Journal of Science and Technology 15, no. 16 (2022): 712–17. https://doi.org/10.17485/IJST/v15i16.88.

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Abstract <strong>Objectives:</strong> Handwritten script identification plays a vital role in processing handwritten data electronically. Most of the methods fail to provide accuracy due to variation in handwriting, hence the classification of the Indic script before providing it to OCR is crucial. The anticipated work helps increase the accuracy by categorizing the handwritten documents as north or South Indic script before further classification. <strong>Methods:</strong> This study has proposed a method, using Gabor filters to extract features from the text image for recogniz

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Singh, Pawan Kumar, Ram Sarkar, Ajith Abraham, and Mita Nasipuri. "A Case Study on Handwritten Indic Script Classification: Benchmarking of the Results at Page, Block, Text-line, and Word Levels." ACM Transactions on Asian and Low-Resource Language Information Processing 21, no. 2 (2022): 1–36. http://dx.doi.org/10.1145/3476102.

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Handwritten script classification is still considered as a challenging research problem in the domain of document image analysis. Although some research attempts have been made by the researchers for solving the challenging issues, a comprehensive solution is yet to be achieved. The case study, undertaken here, analyzes the performances of various state-of-the art handwritten script classification methods for Indian scripts where features, needed for the script classification task, are extracted from the script images at four different granularity levels, i.e., page, block, text line, or word.

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Sproat, Richard. "Brahmi-derived scripts, script layout, and segmental awareness." Written Language and Literacy 9, no. 1 (2006): 45–66. http://dx.doi.org/10.1075/wll.9.1.05spr.

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In earlier work (Sproat 2000), I characterized the layout of symbols in a script in terms of a calculus involving two dimensional catenation operators: I claimed that leftwards, rightwards, upwards, downwards and surrounding catenation are sufficient to describe the layout of any script. In the first half of this paper I analyze four Indic alphasyllabaries — Devanagari, Oriya, Kannada and Tamil — in terms of this model. A crucial claim is that despite the complexities of layout in alphasyllabic scripts, they are essentially no different in nature than alphabetic scripts, such as Latin. The sec

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Obaidullah, Sk Md, K. C. Santosh, Nibaran Das, Chayan Halder, and Kaushik Roy. "Handwritten Indic Script Identification in Multi-Script Document Images: A Survey." International Journal of Pattern Recognition and Artificial Intelligence 32, no. 10 (2018): 1856012. http://dx.doi.org/10.1142/s0218001418560128.

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Script identification is crucial for automating optical character recognition (OCR) in multi-script documents since OCRs are script-dependent. In this paper, we present a comprehensive survey of the techniques developed for handwritten Indic script identification. Different pre-processing and feature extraction techniques, including classifiers used for script identification, are categorized and their merits and demerits are discussed. We also provide information about some handwritten Indic script datasets. Finally, we highlight the extensions and/or future scope of works together with challe

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Obaidullah, Sk Md, Chitrita Goswami, K. C. Santosh, Nibaran Das, Chayan Halder, and Kaushik Roy. "Separating Indic Scripts with matra for Effective Handwritten Script Identification in Multi-Script Documents." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 05 (2017): 1753003. http://dx.doi.org/10.1142/s0218001417530032.

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We present a novel approach for separating Indic scripts with ‘matra’, which is used as a precursor to advance and/or ease subsequent handwritten script identification in multi-script documents. In our study, among state-of-the-art features and classifiers, an optimized fractal geometry analysis and random forest are found to be the best performer to distinguish scripts with ‘matra’ from their counterparts. For validation, a total of 1204 document images are used, where two different scripts with ‘matra’: Bangla and Devanagari are considered as positive samples and the other two different scri

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Singh, Pawan Kumar, Supratim Das, Ram Sarkar, and Mita Nasipuri. "Line Parameter based Word-Level Indic Script Identification System." International Journal of Computer Vision and Image Processing 6, no. 2 (2016): 18–41. http://dx.doi.org/10.4018/ijcvip.2016070102.

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In this paper, a line parameter based approach is presented to identify the handwritten scripts written in eight popular scripts. Since Optical Character Recognition (OCR) engines are usually script-dependent, automatic text recognition in multi-script environment requires a pre-processing module that helps identifying the scripts before processing the same through the respective OCR engine. The work becomes more challenging when it deals with handwritten document which is still a less explored research area. In this paper, a line parameter based approach is presented to identify the handwritt

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CHOKSI, NISHAANT. "From Language to Script: Graphic practice and the politics of authority in Santali-language print media, eastern India." Modern Asian Studies 51, no. 5 (2017): 1519–60. http://dx.doi.org/10.1017/s0026749x16000470.

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AbstractThis article discusses the way in which assemblages of technologies, political institutions, and practices of exchange have rendered both language and script a site for an ongoing politics of authority among Santals, an Austro-Asiatic speaking Adivasi (Scheduled Tribe) community spread throughout eastern India. It focuses particularly on the production of Santali-language print artefacts, which, like its dominant language counterparts, such as Bengali, has its roots in colonial-era Christian missions. However, unlike dominant languages, Santali-language media has been characterized by

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Susan, Seba, and Jatin Malhotra. "Recognising Devanagari Script by Deep Structure Learning of Image Quadrants." DESIDOC Journal of Library & Information Technology 40, no. 05 (2020): 268–71. http://dx.doi.org/10.14429/djlit.40.05.16336.

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   Ancient Indic languages were written in the Devanagari script from which most of the modern-day Indic writing systems have evolved. The digitisation of ancient Devanagari manuscripts, now archived in national museums, is a part of the language documentation and digital archiving initiative of the Government of India. The challenge in digitizing these handwritten scripts is the lack of adequate datasets for training machine learning models. In our work, we focus on the Devanagari script that has 46 categories of characters that makes training a difficult task, especially wh

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Researcher. "COMPARING MULTILINGUAL LANGUAGE MODELS ON INDIC NEWS HEADLINE CLASSIFICATION." International Journal of Artificial Intelligence Research and Development (IJAIRD) 2, no. 2 (2024): 122–28. https://doi.org/10.5281/zenodo.14190096.

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This work explores the problem of news headline classification in Natural Language Processing (NLP). This is a widely studied topic in the realm of NLP. However, limited work has been done on multilingual text classification (specific to Indic languages). Indic language models focus primarily on widely spoken Indian Languages. The performance of these multilingual language models is measured on the Indic News Headline dataset (iNLTK). This dataset also serves as a genre classification dataset (containing ten different genres/categories of headlines). This is done for languages such as Gujarati

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Ghosh, Rajib, Partha Pratim Roy, and Prabhat Kumar. "Smart Device Authentication Based on Online Handwritten Script Identification and Word Recognition in Indic Scripts Using Zone-Wise Features." International Journal of Information System Modeling and Design 9, no. 1 (2018): 21–55. http://dx.doi.org/10.4018/ijismd.2018010102.

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Secure authentication is a vital component for device security. The most basic form of authentication is by using passwords. With the evolution of smart devices, selecting stronger and unbreakable passwords have become a challenging task. Such passwords if written in native languages tend to offer improved security since attackers having no knowledge of such scripts finding it hard to crack. This article proposes two zone-wise feature extraction approaches - zone-wise structural and directional (ZSD) and zone-wise slopes of dominant points (ZSDP), to recognize online handwritten script and wor

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Obaidullah, S. K., K. C. Santosh, Chayan Halder, Nibaran Das, and Kaushik Roy. "Word-Level Multi-Script Indic Document Image Dataset and Baseline Results on Script Identification." International Journal of Computer Vision and Image Processing 7, no. 2 (2017): 81–94. http://dx.doi.org/10.4018/ijcvip.2017040106.

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Document analysis research starves from the availability of public datasets. Without publicly available dataset, one cannot make fair comparison with the state-of-the-art methods. To bridge this gap, in this paper, the authors propose a word-level document image dataset of 13 different Indic languages from 11 official scripts. It is composed of 39K words that are equally distributed i.e., 3K words per language. For a baseline results, five different classifiers: multilayer perceptron (MLP), fuzzy unordered rule induction algorithm (FURIA), simple logistic (SL), library for linear classifier (L

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Holle, K. F. "Table of Old and New Indic Alphabets." Written Language and Literacy 2, no. 2 (1999): 167–245. http://dx.doi.org/10.1075/wll.2.2.02hol.

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Editor's note: Although the general policy of this journal is to publish only new research, an exception is being made in the present case, in order to publish a work of unusual value which has been inaccessible to most scholars for a century or more, and which has now been translated into English for the first time. In 1877, K. F. Holle published his Tabel van oud en nieuw-indische alphabetten, with the support of the Batavia Society of Arts and Letters (the Batavia of that period is the Jakarta of today); it was printed by C. Lang at Buitenzorg, Java. Hoik's "Table" is spread over 49 pages f

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V.Khangar, Smita, and Latesh G. Malik. "Handwritten Text Image Compression for Indic Script Document." International Journal of Computer Applications 47, no. 5 (2012): 11–16. http://dx.doi.org/10.5120/7183-9888.

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Radhakrishnan, CV, KV Rajeesh, and KH Hussain. "Metafont/MetaPost and a complex Indic script: Malayalam." TUGboat 44, no. 2 (2023): 297–314. http://dx.doi.org/10.47397/tb/44-2/tb137radhakrishnan-malayalam.

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Singh, Pawan Kumar, Ram Sarkar, and Mita Nasipuri. "Offline Script Identification from multilingual Indic-script documents: A state-of-the-art." Computer Science Review 15-16 (February 2015): 1–28. http://dx.doi.org/10.1016/j.cosrev.2014.12.001.

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Shaik Moinuddin Ahmed, Et al. "Handwritten OCR for Indic Scripts: A Comprehensive Overview of Machine Learning and Deep Learning Techniques." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9 (2023): 2245–58. http://dx.doi.org/10.17762/ijritcc.v11i9.9230.

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The potential uses of cursive optical character recognition, commonly known as OCR, in a number of industries, particularly document digitization, archiving, even language preservation, have attracted a lot of interest lately. In the framework of optical character recognition (OCR), the goal of this research is to provide a thorough understanding of both cutting-edge methods and the unique difficulties presented by Indic scripts. A thorough literature search was conducted in order to conduct this study, during which time relevant publications, conference proceedings, and scientific files were

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Mukhopadhyay, Anirban, Pawan Kumar Singh, Ram Sarkar, and Mita Nasipuri. "Handwritten Indic Script Recognition Based on the Dempster–Shafer Theory of Evidence." Journal of Intelligent Systems 29, no. 1 (2018): 264–82. http://dx.doi.org/10.1515/jisys-2017-0431.

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Abstract In a multilingual country like India, script recognition is an important pre-processing footstep necessary for feeding any document to an optical character recognition (OCR) engine, which is, in general, script specific. The present work evaluates the performance of an ensemble of two MLP (multi-layer perceptron) classifiers, each trained on different feature sets. Here, two complementary sets of features, namely, gray-level co-occurrence matrix (GLCM) and Gabor wavelets transform coefficients are extracted from each of the handwritten text-line and word images written in 12 official

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Brijeshkumar Y. Panchal. "Examine the Opportunities and Challenges of Large Language Model (LLM) For Indic Languages." Journal of Information Systems Engineering and Management 10, no. 26s (2025): 301–25. https://doi.org/10.52783/jisem.v10i26s.4236.

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Large Language Models like GPT and BERT have made significant advancements in NLP, particularly in text generation, translation, and summarization. However, their application in Indic languages remains relatively unexplored due to unique linguistic challenges such as complex morphology, diverse scripts, and limited digitized resources. This systematic literature review follows PRISMA guidelines to identify, analyze, and evaluate existing research on the opportunities and challenges of LLMs for Indic languages. The review covers relevant publications from databases like Web of Science, IEEE Xpl

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Brijeshkumar Y. Panchal. "Examine the Opportunities and Challenges of Large Language Model (LLM) For Indic Languages." Journal of Information Systems Engineering and Management 9, no. 4s (2024): 1384–408. https://doi.org/10.52783/jisem.v9i4s.11887.

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Large Language Models like GPT and BERT have made significant advancements in NLP, particularly in text generation, translation, and summarization. However, their application in Indic languages remains relatively unexplored due to unique linguistic challenges such as complex morphology, diverse scripts, and limited digitized resources. This systematic literature review follows PRISMA guidelines to identify, analyze, and evaluate existing research on the opportunities and challenges of LLMs for Indic languages. The review covers relevant publications from databases like Web of Science, IEEE Xpl

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Singh, Pawan Kumar, Ram Sarkar, and Mita Nasipuri. "Indic script identification from handwritten document images." International Journal of Intelligent Systems Technologies and Applications 18, no. 3 (2019): 303. http://dx.doi.org/10.1504/ijista.2019.099341.

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Singh, Pawan Kumar, Mita Nasipuri, and Ram Sarkar. "Indic script identification from handwritten document images." International Journal of Intelligent Systems Technologies and Applications 18, no. 3 (2019): 303. http://dx.doi.org/10.1504/ijista.2019.10020091.

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Acri, Andrea, and Arlo Griffiths. "The Romanisation of Indic Script Used in Ancient Indonesia." Bijdragen tot de taal-, land- en volkenkunde / Journal of the Humanities and Social Sciences of Southeast Asia 170, no. 2-3 (2014): 365–78. http://dx.doi.org/10.1163/22134379-17002005.

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Obaidullah, Sk Md, Chayan Halder, K. C. Santosh, Nibaran Das, and Kaushik Roy. "PHDIndic_11: page-level handwritten document image dataset of 11 official Indic scripts for script identification." Multimedia Tools and Applications 77, no. 2 (2017): 1643–78. http://dx.doi.org/10.1007/s11042-017-4373-y.

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B.Hari, Kumar, and P.Chitra. "International, National and Local Languages OCR Segmentation of Running Hand Scripts." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 3 (2020): 169–73. https://doi.org/10.35940/ijeat.C5028.029320.

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Script Segmentation of running hand scripts is a complicated job because of different hand writing styles and complex formational quality. Segmentation of moving hand script in Indian language is a difficult assignment .The incident of a title, crossway nature in the mid section & half nature makes the segmentation process is more difficult. Sometimes, the import space and noises make line shatter a hard task. Without disconnecting the nudge characters, it will be complicated to recognize the character; hence shatter is needed for the moving texts in a word. So, the technique, according to

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Chirimilla, Ramya, and Vishnu Vardhan B. "A Survey of Optical Character Recognition Techniques on Indic Script." ECS Transactions 107, no. 1 (2022): 6507–14. http://dx.doi.org/10.1149/10701.6507ecst.

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Optical Character Recognition (OCR) is a technique that converts printed text and images into a digitized form, which can be manipulated by a machine. It has many application sectors like Banking, Financial, Legal applications, etc. Initially researchers were addressed and proposed many algorithms in image processing for character recognition and mapping. Most of the researchers focused on the Latin script English as it was supported by the Encoding standard ASCII. Later, people start realizing that OCR techniques for other languages are also gaining momentum these days. With the advent of tec

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Miyake, Marc. "Studies in Pyu Phonology, ii: Rhymes." Bulletin of Chinese Linguistics 11, no. 1-2 (2018): 34–76. http://dx.doi.org/10.1163/2405478x-01101008.

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The extinct Pyu language was spoken during the first millennium CE and the early centuries of the second millennium CE in what is now Upper Burma. Pyu appears to be Sino-Tibetan on the basis of its basic vocabulary. It survives in inscriptions in an Indic script. This study reconstructs Pyu rhymes on the basis of spellings in those inscriptions and concludes that Pyu was an atonal language with 7 vowels and 18 final consonants. Some previous scholars have interpreted the subscript dots of the Pyu script as tone markers, but this study argues that they indicate fricative initials.

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Vijayapal Reddy, P., B. Vishnu Vardhan, and A. Govardhan. "Analysis of BMW Model for Title Word Selection on Indic Script." International Journal of Computer Applications 18, no. 8 (2011): 21–25. http://dx.doi.org/10.5120/2304-2915.

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Obaidullah, Sk Md, Nibaran Das, and Kaushik Roy. "Convolution Based Technique for Indic Script Identification from Handwritten Document Images." International Journal of Image, Graphics and Signal Processing 7, no. 5 (2015): 49–57. http://dx.doi.org/10.5815/ijigsp.2015.05.06.

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Obaidullah, Sk Md, Amitava Bose, Himadri Mukherjee, K. C. Santosh, and Nibaran Das. "Extreme learning machine for handwritten Indic script identification in multiscript documents." Journal of Electronic Imaging 27, no. 05 (2018): 1. http://dx.doi.org/10.1117/1.jei.27.5.051214.

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Bhunia, Ayan Kumar, Subham Mukherjee, Aneeshan Sain, Ankan Kumar Bhunia, Partha Pratim Roy, and Umapada Pal. "Indic handwritten script identification using offline-online multi-modal deep network." Information Fusion 57 (May 2020): 1–14. http://dx.doi.org/10.1016/j.inffus.2019.10.010.

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Singh, Pawan Kumar, Supratim Das, Ram Sarkar, and Mita Nasipuri. "Feature Selection Using Harmony Search for Script Identification from Handwritten Document Images." Journal of Intelligent Systems 27, no. 3 (2018): 465–88. http://dx.doi.org/10.1515/jisys-2016-0070.

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Abstract The feature selection process can be considered a problem of global combinatorial optimization in machine learning, which reduces the irrelevant, noisy, and non-contributing features, resulting in acceptable classification accuracy. Harmony search algorithm (HSA) is an evolutionary algorithm that is applied to various optimization problems such as scheduling, text summarization, water distribution networks, vehicle routing, etc. This paper presents a hybrid approach based on support vector machine and HSA for wrapper feature subset selection. This approach is used to select an optimiz

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Mukhopadhyay, Anirban, Pawan Singh, Ram Sarkar, and Mita Nasipuri. "A Study of Different Classifier Combination Approaches for Handwritten Indic Script Recognition." Journal of Imaging 4, no. 2 (2018): 39. http://dx.doi.org/10.3390/jimaging4020039.

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Adhwaith A M, Adhwaith A. M., Irin Jossy, Mahima Rachel Bijoy, Nikitha Liz Koshy, and Sreelekshmi K. R. Sreelekshmi K R. "Handwritten Text Recognition: A Survey of OCR Techniques." International Journal of Advances in Engineering and Management 6, no. 11 (2024): 205–20. https://doi.org/10.35629/5252-0611205220.

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Optical Character Recognition of handwritten texts has witnessed remarkable advancements with the integration of deep learning and machine learning techniques. Recognizing handwritten characters poses unique challenges due to script variability, linguistic diversity, and the complexities of historical documents. This survey explores recent developments in OCR for various languages, emphasizing innovative approaches such as Convolutional Neural Networks, attention mechanisms, and transfer learning. We analyze methodologies that enhance character recognition accuracy across languages, including

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Ukil, Soumya, Swarnendu Ghosh, Sk Md Obaidullah, K. C. Santosh, Kaushik Roy, and Nibaran Das. "Improved word-level handwritten Indic script identification by integrating small convolutional neural networks." Neural Computing and Applications 32, no. 7 (2019): 2829–44. http://dx.doi.org/10.1007/s00521-019-04111-1.

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Singh, Tajinder Pal, Sheifali Gupta, and Meenu Garg. "Machine Learning: A Review on Supervised Classification Algorithms and their Applications to Optical Character Recognition in Indic Scripts." ECS Transactions 107, no. 1 (2022): 6233–50. http://dx.doi.org/10.1149/10701.6233ecst.

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Machine learning is a common application of artificial intelligence, which gives machines the ability to learn from data automatically and improve with experience without being explicitly programmed. Supervised learning is one of two broad areas of machine learning that deal with the task of learning a function for a current model based on past data training on input-output pairs of examples. This function enables the model to predict future outcomes for new inputs. Regression and classification are two supervised machine learning problems. Classification is the most common task that intellige

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Tabain, Marija, and Anthony Jukes. "Makasar." Journal of the International Phonetic Association 46, no. 1 (2016): 99–111. http://dx.doi.org/10.1017/s002510031500033x.

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Makasar is an Austronesian language belonging to the South Sulawesi subgroup within the large Western-Malayo Polynesian family. It is spoken by about two million people in the province of South Sulawesi in Indonesia, and is the second largest language on the island of Sulawesi (behind Bugis, with about three million speakers). The phonology is notable for the large number of geminate and pre-glottalised consonant sequences, while the morphology is characterised by highly productive affixation and pervasive encliticisation of pronominal and aspectual elements. The language has a literary tradit

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Kore, Vrinda, Dhruva G, Sahana Rao, Vijitha M, and P. Preethi. "A Systematic Framework for Sanskrit Character Recognition Using Deep Learning." ELCVIA Electronic Letters on Computer Vision and Image Analysis 24, no. 1 (2025): 81–103. https://doi.org/10.5565/rev/elcvia.1850.

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Sanskrit is widely acknowledged to be among the world’s oldest surviving classical languages, and yet its usage has continued to decline unabated in the present milieu. Such insidious erosion of popularity is directly attributable to the absence of native speakers of the language and the perceived inaccessibility of Sanskrit to contemporary audiences. Notwithstanding, the language remains historically and culturally inseparable from the subcontinent, with numerous religious manuscripts, epigraphical inscriptions, edicts and scientific literature written in the Sanskrit script. Attempts made to

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Miyake, Marc. "Studies in Pyu phonology, I." Language and Linguistics / 語言暨語言學 22, no. 1 (2020): 28–70. http://dx.doi.org/10.1075/lali.00077.miy.

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Abstract The extinct Pyu language was spoken during the first millennium CE and the early centuries of the second millennium CE in what is now Upper Burma. It has been classified as Sino-Tibetan on the basis of basic vocabulary, but its precise position within the family remains unknown. It survives in inscriptions in an Indic script. In this study, the first of its kind, I begin to reconstruct Pyu phonology on the basis of spellings in those inscriptions. I propose that Pyu was a sesquisyllabic language with 7 preinitials and 43 or 44 initials.

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Wu, Yuling. "Exploring Four Block-Printed Indic Script Mahāpratisarā Dhāraṇī (Chinese: 大隨求陀羅尼) Amulets Discovered in China". Religions 16, № 5 (2025): 635. https://doi.org/10.3390/rel16050635.

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This article examines four block-printed Mahāpratisarā dhāraṇī amulets from late Tang to early Song China, highlighting how Sanskrit-script texts circulated in everyday religious life. Through a philological and visual analysis, it reveals a decentralised dhāraṇī culture shaped by variant bījākṣara (seed syllable) arrangements, divergent textual recensions, and diverse ritual uses—from burial and temple consecration to daily wear and cave enshrinement. Rather than static texts, these amulets reflect dynamic interactions among sacred sound, material form, and vernacular Buddhist practice, offer

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Obaidullah, Sk Md, K. C. Santosh, Chayan Halder, Nibaran Das, and Kaushik Roy. "Automatic Indic script identification from handwritten documents: page, block, line and word-level approach." International Journal of Machine Learning and Cybernetics 10, no. 1 (2017): 87–106. http://dx.doi.org/10.1007/s13042-017-0702-8.

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Kundu, Soumyadeep, Sayantan Paul, Pawan Kumar Singh, Ram Sarkar, and Mita Nasipuri. "Understanding NFC-Net: a deep learning approach to word-level handwritten Indic script recognition." Neural Computing and Applications 32, no. 12 (2019): 7879–95. http://dx.doi.org/10.1007/s00521-019-04235-4.

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Riddhi Dave and Dr. Sheshang Degadwala. "Hybrid Modeling for Effective Text Spotting in Gujarati Language." International Journal of Scientific Research in Science and Technology 12, no. 3 (2025): 411–16. https://doi.org/10.32628/ijsrst2512356.

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This paper presents a novel hybrid modeling approach for effective text spotting specifically tailored to the Gujarati language, achieving a high accuracy of 91.8% while maintaining efficient training time of only 18 minutes. The proposed hybrid model synergistically combines convolutional neural networks (CNN) for feature extraction and transformer-based architectures for contextual understanding, optimizing both recognition accuracy and computational efficiency. Gujarati, with its complex script and unique character shapes, presents challenges such as cursive and ligature forms, which the hy

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Mehulkumar Dalwadi. "Detection of Gujarati Handwritten Characters using Artificial Intelligence Techniques: Challenges, Opportunities, and Future Directions." Journal of Information Systems Engineering and Management 10, no. 24s (2025): 381–90. https://doi.org/10.52783/jisem.v10i24s.3913.

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The handwritten character recognition (HCR) has grown significance in document digitisation, multilingual text processing, and automated form interpretation. Among the different Indic scripts it is relatively underexplored, despite a large population of speakers. This paper provides a comprehensive review of the state of the art of Artificial Intelligence (AI) models for the detection and recognition of characters from Gujarati handwritten text. We critically review over 30 work references focusing on the benefits and limitations of traditional machine learning (ML) methods, hybrid models and

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Maurya, R. K., and S. R. Maurya. "Recognition of a Medieval Indic-Modi Script using Empirically Determined Heuristics in Hybrid Feature Space." International Journal of Computer Sciences and Engineering 6, no. 2 (2018): 136–42. http://dx.doi.org/10.26438/ijcse/v6i2.136142.

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Dragoni, Federico, Niels Schoubben та Michaël Peyrot. "The Formal Kharoṣṭhī script from the Northern Tarim Basin in Northwest China may write an Iranian language". Acta Orientalia Academiae Scientiarum Hungaricae 73, № 3 (2020): 335–73. http://dx.doi.org/10.1556/062.2020.00015.

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ABSTRACTBuilding on collaborative work with Stefan Baums, Ching Chao-jung, Hannes Fellner and Georges-Jean Pinault during a workshop at Leiden University in September 2019, tentative readings are presented from a manuscript folio (T II T 48) from the Northern Tarim Basin in Northwest China written in the thus far undeciphered Formal Kharoṣṭhī script. Unlike earlier scholarly proposals, the language of this folio cannot be Tocharian, nor can it be Sanskrit or Middle Indic (Gāndhārī). Instead, it is proposed that the folio is written in an Iranian language of the Khotanese-Tumšuqese type. Severa

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Padmavathi Pragada. "Automated LSTM Based Deep Learning Model for Handwritten Telugu Answer Script Analysis." Communications on Applied Nonlinear Analysis 32, no. 8s (2025): 745–62. https://doi.org/10.52783/cana.v32.3796.

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The growing demand for automated evaluation systems in educational environments, especially for languages with complex scripts like Telugu, drives the motivation for this research. Traditional handwriting recognition methods for Telugu have faced challenges with limited accuracy and adaptability, particularly in real-world educational scenarios. These limitations often result in reduced precision in character and sentence recognition, along with increased processing delays. This study proposes a novel system for the automated evaluation of handwritten Telugu answer scripts. The model incorpora

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Khan, Muhammad Yaseen. "Edit distance-based search approach for retrieving element-wise prosody/rhymes in Hindi-Urdu poetry." Indian Journal of Science and Technology 13, no. 39 (2020): 4189–201. http://dx.doi.org/10.17485/ijst/v13i39.1489.

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Background: Prosody (rhyming words) is a connatural element of poetry, throughout its reach, across thousands of languages in the world. Since medieval era, the Indic poetry (principally the Hindi/Urdu poetry) has created an impactful flamboyance w.r.t the subjects, styles, and other creative aspects in poetry. Besides the message of heartfelt poetry, we see the Qafiya (i.e., rhyming words) is the core element, without which we may not consider anything Hindi/Urdu poetry but merely a piece of writing; alongside it, Radif (i.e., a phrasal suffix to qafiya) is also considered next to the intrins

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Muhammad, Yaseen Khan, Adil Rao Muhammad, Wasi Shaukat, Ahmed Minai Twaha, and Muhammad Khaliq-ur-Rahman Raazi Syed. "Edit distance-based search approach for retrieving element-wise prosody/rhymes in Hindi-Urdu poetry." Indian Journal of Science and Technology 13, no. 39 (2020): 4189–201. https://doi.org/10.17485/IJST/v13i39.1489.

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Abstract <strong>Background:</strong> Prosody (rhyming words) is a connatural element of poetry, throughout its reach, across thousands of languages in the world. Since medieval era, the Indic poetry (principally the Hindi/Urdu poetry) has created an impactful flamboyance w.r.t the subjects, styles, and other creative aspects in poetry. Besides the message of heartfelt poetry, we see the Qafiya (i.e., rhyming words) is the core element, without which we may not consider anything Hindi/Urdu poetry but merely a piece of writing; alongside it, Radif (i.e., a phrasal suffix to qafiya) is also

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Pande, Hemlata, and Hoshiyar S. Dhami. "Analysis and Mathematical Modelling of the Pattern of Occurrence of VariousDevanāgariLetter Symbols according to the Phonological Inventory of Indic Script in Hindi Language." Journal of Quantitative Linguistics 22, no. 1 (2014): 22–43. http://dx.doi.org/10.1080/09296174.2014.974457.

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